On the algorithm aspect, there have been many developments for SELD, inside and outside the DCASE Challenges, in the areas of data augmentation, feature engineering, model architectures, and output formats. In 2015, an early monophonic SELD work by Hirvonen [1]} formulated SELD as a classification task, where each output class represents a sound class-location pair. In 2018, Adavanne et al. pioneered a seminal polyphonic SELD work that used a single-input multiple-output convolutional recurrent neural network (CRNN) model, SELDnet, to jointly detect sound events and estimate the corresponding DOAs [2]}. In 2019, Cao et al. proposed a two-stage strategy by training separate SED and DOA models [3]}, then using the SED outputs as masks to select the DOA outputs, significantly outperforming the jointly-trained SELDnet. Cao et al. later proposed an end-to-end SELD network [4]} that used soft parameter sharing between the SED and DOAE encoder branches and output trackwise predictions. An improved version of this network [5]} replaced the bidirectional gated recurrent units (GRU) with multi-head self-attention (MHSA) to decode the SELD outputs [5]}. In 2020, Shimada et al. proposed a new output format for SELD which unified SED and DOAE into one loss function [7]}. This was amongst the few works which successfully used the linear-frequency for spectrograms and interchannel phase differences as input features, instead of the mel spectrograms. A new CNN architecture, D3Net [8]}, was adapted into a CRNN for this work and showed promising results. In another research direction, Nguyen et al. proposed to solve SED and DOAE separately, use a bidirectional GRU to match the SED and DOAE output sequences, then produce event-wise SELD outputs [9]}, [10]}. This was based on the observation that different sound events often have different onsets and offsets, resulting in temporal matching in the SED and DOAE output sequences. In 2021, Nguyen et al. proposed a new input feature, SALSA, which spectrotemporally aligns the spatial cues with the signal power in the linear-frequency scale to improve SELD performance [11]}.
